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Related Concept Videos

Classification of Bones01:18

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Classification of Connective Tissues01:30

Classification of Connective Tissues

The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense.
Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...

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Related Experiment Videos

A Website-Based Classification of Cervical Spine Conditions Using Convolutional Neural Network.

Ee Herng Loh1, Kim Gaik Tay2, Mohd Norzali Haji Mohd1

  • 1Electronic Engineering Department, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, 86400, Malaysia.

Journal of Imaging Informatics in Medicine
|July 13, 2026
PubMed
Summary

This study introduces an automated web prototype using deep learning for cervical spine X-ray analysis, improving diagnostic accuracy for spinal conditions like lordosis and kyphosis. The convolutional neural network (CNN) model achieved higher accuracy than traditional Cobb angle measurements.

Keywords:
Cervical spine alignmentConvolutional neural networkFine-tuningHyperparameter optimisationMedical image classificationTransfer learning

Related Experiment Videos

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Spine Surgery

Background:

  • Manual assessment of cervical spine alignment from X-rays is crucial but subjective and time-consuming.
  • Automated methods are needed to improve the accuracy and efficiency of diagnosing cervical spine disorders.

Purpose of the Study:

  • To develop and evaluate an automated web-based prototype for cervical spine condition classification using deep learning.
  • To compare the performance of a convolutional neural network (CNN) against traditional Cobb angle measurements.

Main Methods:

  • A fivefold cross-validation framework was used with transfer learning, fine-tuning, and hyperparameter optimization (Optuna).
  • Two pre-trained CNN models, ResNet152V2 and ConvNeXt Tiny, were evaluated.
  • The best performing model was deployed via a Gradio web interface.

Main Results:

  • ResNet152V2 achieved 86.0% accuracy, outperforming ConvNeXt Tiny and demonstrating greater stability.
  • CNN-based classification (96.67% accuracy) significantly outperformed rule-based Cobb angle classification (88.33% accuracy).

Conclusions:

  • An optimized deep learning model integrated into a web prototype is feasible for real-time cervical spine research.
  • The developed CNN approach offers a more accurate and reliable method for cervical spine disorder assessment compared to traditional techniques.